Multivariate relative risk (RR) of diabetes according to joint classifications of plasma levels of high-sensitivity C-reactive protein (hsCRP [to convert C-reactive protein to nanomoles per liter, multiply by 9.524]), tumor necrosis factor α receptor 2 (TNF-α–R2), and IL-6 (interleukin 6). Plasma levels of TNF-α–R2 and IL-6 were categorized into tertiles, respectively. Ranges of less than 1, 1 to 3, and greater than or equal to 3 mg/L were used for hsCRP levels. The adjusted RR of type 2 diabetes among women in each category is shown relative to women with the lowest tertiles for both biomarkers. The model was adjusted for matching factors, body mass index, alcohol intake, level of physical activity, cigarette smoking status, use or nonuse of postmenopausal hormone therapy, and presence or absence of family history of diabetes. A, Joint effect of TNF-α–R2 and IL-6. B, Joint effect of TNF-α–R2 and hsCRP. C, Joint effect of IL-6 and hsCRP.
Liu S, Tinker L, Song Y, Rifai N, Bonds DE, Cook NR, Heiss G, Howard BV, Hotamisligil GS, Hu FB, Kuller LH, Manson JE. A Prospective Study of Inflammatory Cytokines and Diabetes Mellitus in a Multiethnic Cohort of Postmenopausal Women. Arch Intern Med. 2007;167(15):1676-1685. doi:10.1001/archinte.167.15.1676
Copyright 2007 American Medical Association. All Rights Reserved. Applicable FARS/DFARS Restrictions Apply to Government Use.2007
Inflammatory cytokines, including tumor necrosis factor α, IL-6 (interleukin 6), and high-sensitivity C-reactive protein (hsCRP), have been related to both insulin resistance and type 2 diabetes mellitus. However, prospective studies that comprehensively assess their roles in the development of type 2 diabetes are few, especially in minority populations.
Among 82 069 postmenopausal women aged 50 to 79 years without cardiovascular disease or diabetes mellitus who participated in the Women's Health Initiative Observational Study, we prospectively examined the relationships of plasma levels of tumor necrosis factor α receptor 2, IL-6, and hsCRP to diabetes risk. During a median follow-up period of 5.9 years, 1584 women who had clinical diabetes were matched by age, ethnicity, clinical center, time of blood draw, and duration of follow-up to 2198 study participants who were free of the disease.
After adjustment for matching factors and known diabetes risk factors, all 3 markers were significantly associated with increased diabetes risk; the estimated relative risks comparing the highest with the lowest quartiles were 1.47 (95% confidence interval [CI], 1.10-1.97) for tumor necrosis factor α receptor 2, 3.08 (95% CI, 2.25-4.23) for IL-6, and 3.46 (95% CI, 2.50-4.80) for hsCRP (P for trend, <.01 for all biomarkers). When mutually adjusted, IL-6 and hsCRP remained significant in each ethnic group. While no statistically significant interactions were observed between ethnicity and these biomarkers on diabetes risk, there were consistent trends for the associations of hsCRP and IL-6 with increased diabetes risk in all ethnic groups.
These prospective data showed that elevated levels of IL-6 and hsCRP were consistently and significantly associated with an increased risk of clinical diabetes in postmenopausal women.
Low-grade chronic inflammation, as reflected by elevated circulating levels of inflammatory cytokines,1- 3 may promote insulin resistance in liver, skeletal muscle, and vascular endothelium,4,5 ultimately leading to the clinical expression of both type 2 diabetes mellitus and cardiovascular disease (CVD).6 To date, cross-sectional studies have associated plasma levels of high-sensitivity C-reactive protein (hsCRP),7- 16 IL-6 (interleukin 6),7,8,15,17,18 and soluble tumor necrosis factor α (TNF-α) receptors7- 9,15 with obesity and insulin resistance. Similar relationships have also been observed between these cytokines and other metabolic risk factors such as dyslipidemia and hypertension among nondiabetic individuals. In several prospective studies, however, the relationships of these inflammatory markers to risk of clinical diabetes have been inconsistent.19- 33 Moreover, while TNF-α receptors, IL-6, and hsCRP may each arguably serve as a sensitive marker of an underlying inflammatory state, previous studies have mainly focused on hsCRP.19- 21,23- 26,28,30,32,33 Furthermore, whereas ethnic differences for levels of inflammatory markers such as hsCRP have been reported,20,34 to our knowledge no studies to date have comprehensively assessed the relationships between these biomarkers and risk of type 2 diabetes according to ethnicity.
We therefore prospectively examined the associations between baseline levels of inflammatory cytokines and risk of clinical diabetes in apparently healthy women aged 50 to 79 years from the Women's Health Initiative Observational Study (WHIOS), an ethnically diverse cohort of postmenopausal women, including whites, blacks, Hispanics, and Asians/Pacific Islanders.
The WHIOS is an ongoing longitudinal study that was designed to examine the association between clinical, socioeconomic, behavioral, and dietary factors and subsequent health outcomes. Details of the rationale, eligibility, and other design aspects have been published elsewhere.35,36 In brief, between September 1994 and December 1998, the WHIOS enrolled a total of 93 676 women aged 50 to 79 years at 40 clinical centers throughout the United States. At baseline, women completed screening and enrollment questionnaires, underwent a physical examination, and provided a fasting blood specimen. Of the 93 676 postmenopausal women enrolled into the WHIOS cohort, 82 069 had no history of diabetes or CVD. The study was reviewed and approved by human subjects review committees at each participating institution, and signed informed consent was obtained from all women enrolled.
The methods of data collection and validation have been reported previously.35,36 Baseline information was collected using a standardized protocol implemented by centrally trained clinic staff, with a quality assurance program for uniformity across the entire study population. Self-administered questionnaires at study entry included information on age, ethnicity, education, income, reproductive history, detailed history of hormone replacement therapy and/or use of oral contraceptives, family history of CVD and diabetes, current prescription and over-the-counter medication use, use of vitamin and mineral supplements, smoking status, physical activity, alcohol use, and a detailed dietary assessment using a standardized food frequency questionnaire. During the initial screening visit, anthropometric measurements, blood pressure levels, and fasting blood specimens were also obtained from the participants. The women were instructed to fast overnight for at least 12 hours, to avoid taking any oral medications other than those used for diabetes, and to avoid vigorous exercise and smoking before the blood draw.
Ethnicity was determined by self-report and identified as white, not of Hispanic origin; black/African American; Hispanic/Latino; American Indian or Alaskan Native; Asian/Pacific Islander; or unknown (none of the above). Family history of diabetes was defined by self-report of diabetes in a first-degree relative. Smoking status (nonsmoker, former smoker, or current smoker) was determined from lifetime smoking of at least 100 cigarettes, current daily cigarette smoking, and smoking cessation. Physical activity was quantified by the number of weekly episodes of mild, moderate, or strenuous recreational physical activity. Alcohol intake habits were assessed, and alcohol consumption was computed from a food frequency questionnaire. Weight was measured to the nearest 0.1 kg on a balance beam scale, with the participant dressed in indoor clothing without shoes. Height was measured to the nearest 0.1 cm using a wall-mounted stadiometer. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared.
Throughout the entire WHIOS, diabetes was consistently defined a priori as self-report of clinical cases for women reporting diagnosis when they were not pregnant and those reporting diabetes treatments by diet, oral hypoglycemic medications, or insulin. Participants were followed up annually with self-administered questionnaires (medical history and exposure updates) and an additional clinical center visit for physical measurements 3 years after enrollment. They completed an annual medical history update form on which they indicated the occurrence of any hospitalization and a wide variety of outcomes, including diabetes. Incident diabetes cases were identified based on postbaseline self-report of first-time use of hypoglycemic medication (oral hypoglycemic agents or insulin), or hospitalization for previously unreported diabetes. Eligible cases were WHIOS participants who provided adequate blood specimens, who were free of reported CVD or diabetes at baseline, and who subsequently reported new diabetes treatment with oral hypoglycemic drugs or insulin or hospitalization for diabetes during a median follow-up period of 5.9 years (mean, 5.5 years). Following the principles of risk-set sampling,37 for each new case controls were selected randomly from women who remained free of CVD and/or diabetes at the time the case was identified during follow-up. Identical exclusion or inclusion criteria were applied to eligible controls who were WHIOS participants, who were free of reported CVD and/or diabetes at baseline, and who provided baseline blood specimens. Controls were further matched to the cases by age (±2.5 years), racial/ethnic group (white, black, Hispanic, and Asian/Pacific Islander), clinical center (geographic location), time of blood draw (±0.10 hours), and length of follow-up. In the current study, 968 cases involving white women were randomly chosen and matched with 1 control subject each. Among 616 incident cases involving ethnic minority women, 366 women were black, 152 were Hispanic, and 98 were Asian/Pacific Islander. The 1:2 matching ratio was used for minorities to increase statistical power. We estimated that we would have greater than 80% statistical power to detect a relative risk (RR) equal to or greater than 1.5 among whites or blacks and an RR equal to or greater than 2.0 among other racial or ethnic groups.
According to a standardized protocol, fasting blood specimens were collected from each participant at baseline and processed locally into separate aliquots containing serum, plasma, and buffy coat. The aliquots were frozen and then shipped to a central repository, where they were kept for long-term storage at −70°C. All biochemical assays were carried out by laboratory staff blinded to case/control status (in Dr Rifai's laboratory). Blood samples from cases and their matched controls were handled identically, shipped in the same batch, and assayed in random order in the same analytical run to reduce systematic bias and interassay variation. Tumor necrosis factor α receptor 2 (TNF-α–R2) was measured by an enzyme-linked immunosorbent assay (R&D Systems, Minneapolis, Minnesota). Interleukin 6 (IL-6) was measured by an ultrasensitive enzyme-linked immunosorbent assay (R&D Systems). High-sensitivity C-reactive protein (hsCRP) was measured on a chemistry analyzer (Hitachi 911; Roche Diagnostics, Indianapolis, Indiana) using an immunoturbidimetric assay with reagents and calibrators (Denka Seiken Co Ltd, Niigata, Japan). The coefficients of variation were 3.5% for TNF-α–R2, 7.6% for IL-6, and 1.61% for hsCRP.
Mixed-effects regression was used to examine mean differences in baseline variables, with case-control cluster modeled as a random effect. A matched χ2 test was used to assess differences in proportions. As a surrogate measure of insulin resistance, the homeostasis model assessment—the product of basal fasting glucose (in millimoles per milliliter) and insulin (microinternational units per milliliter) levels divided by 22.5—was used to estimate baseline insulin resistance (HOMA-IR). For TNF-α–R2, IL-6, hsCRP, and HOMA-IR with markedly skewed distributions, we made logarithmic transformations to enhance compliance with normality assumption for estimating age- and ethnicity-adjusted Pearson partial correlation coefficients.
We divided participants into quartiles according to levels of inflammatory markers among controls and applied conditional logistic regression to estimate the RR and 95% confidence interval (CI) for clinical diabetes using the lowest quartile as the reference. To test for linear trend, we used the median levels of inflammatory markers within quartiles in the controls as a continuous variable. We also estimated the RR per standard deviation increment for each marker assuming a linear relationship (per 791 pg/mL for TNF-α–R2, per 4.24 pg/mL for IL-6, and per 5.3 mg/L for hsCRP). We used conditional logistic regression to adjust for matching factors such as age, ethnicity, clinical center, and time of blood draw. In multivariable analyses, we adjusted for BMI (modeled as a continuous covariate), family history of diabetes (yes or no), smoking (never, past, and current smokers), alcohol intake (never, past, and current drinkers), physical activity (quintiles), and postmenopausal hormone use (yes or no). Further, we included TNF-α–R2, IL-6, and hsCRP simultaneously in the same multivariable model to determine their relative significance after mutual adjustment. To examine whether the associations between inflammatory biomarkers and risk of diabetes differ by ethnicity, we examined these markers in relation to diabetes risk stratified by ethnicity. A log likelihood ratio test was used to test the statistical significance of interactions.
To evaluate the joint relationships between any combinations of 2 biomarkers for the risk of diabetes, we divided the study population into 9 groups according to the following cut points: TNF-α–R2 (tertiles: <2077, 2077-2697, and >2697 pg/mL), IL-6 (tertiles: <1.14, 1.14-2.17, and >2.17 pg/mL), and hsCRP (clinical cut points: <1, 1-3, and >3 mg/L). Compared with women who had the lowest levels for both markers, each subgroup-specific RR was estimated using the same conditional logistic regression analysis. To examine whether the associations between inflammatory biomarkers and diabetes were modified by obesity, we also conducted subgroup analyses stratified by BMI (<25 and ≥25) and waist circumference (<89 and ≥89 cm). The likelihood ratio test was performed to test the statistical significance of interactions.
Because women with clinical diabetes who were treated by hypoglycemic drugs or insulin may represent only 1 specific phenotype (more severe cases) whose pathogenesis may be different from that of other diabetes phenotypes (including the asymptomatic cases), we also modified our definition of diabetes post hoc based on fasting glucose levels obtained at baseline and conducted sensitivity analyses excluding those women who had fasting glucose levels greater than or equal to 126 mg/dL (to convert to millimoles per liter, multiply by 0.0555). To further assess the robustness of our findings, we excluded clinical cases reported during the first year of follow-up. All P values were 2-tailed, and P values less than .05 were considered to indicate statistical significance. All analyses were performed with SAS software (version 9.1; SAS Institute Inc, Cary, North Carolina).
Compared with controls, cases had a greater proportion of traditional risk factors at baseline (Table 1), including a significantly higher BMI, waist circumference, and waist-hip ratio and a lower frequency of physical activity and of moderate alcohol intake. Cases were also more likely to have a family history of diabetes than controls. Similar patterns were observed in each ethnic group. In particular, women with diabetes had significantly higher median levels of TNF-α–R2, IL-6, and hsCRP at baseline compared with controls (TNF-α–R2, 2633 pg/mL vs 2361 pg/mL; IL-6, 2.59 pg/mL vs 1.54 pg/mL; and hsCRP, 4.0 mg/L vs 2.1 mg/L [all P values <.001]).
Levels of inflammatory markers varied across ethnic groups, however (Table 1). On average, the highest relative difference in TNF-α–R2 levels between cases and controls was in whites (10.4%), followed by Hispanics (9.7%), blacks (5.9%), and Asians/Pacific Islanders (2.1%). The relative differences in IL-6 levels were also highest in whites (45%) but lowest in blacks (31%), with similar results for Hispanics (37%) and Asians/Pacific Islanders (37%). The hsCRP levels were higher among whites, blacks, and Hispanics than among Asians/Pacific Islanders in cases and controls, but the relative differences in hsCRP levels between cases and controls did not vary considerably across ethnic groups, with the highest percentages found among whites (52%) and Asians/Pacific Islanders (52%).
Table 2 shows the RRs across quartiles of inflammatory marker levels. In analyses adjusted for matching factors, circulating levels of TNF-α–R2, IL-6, and hsCRP were each positively and significantly associated with diabetes risk. After further adjustment for BMI and other traditional risk factors, the RRs of diabetes in the highest quartile compared with the lowest quartile were 1.47 for TNF-α–R2 (95% CI, 1.10-1.97; P for trend, .003), 3.08 for IL-6 (95% CI, 2.25-4.23; P for trend, <.001), and 3.46 for hsCRP (95% CI, 2.50-4.80; P for trend <.001). Additional adjustment for fasting glucose levels, fasting insulin levels, or HOMA-IR attenuated but did not eliminate these associations with levels of IL-6 and hsCRP. When these 3 inflammatory markers were mutually adjusted, TNF-α–R2 levels were not significantly associated with diabetes risk (Table 2). When we further excluded those cases (n = 737) and controls (n = 27) with fasting glucose levels equal to or greater than 126 mg/dL measured once at baseline, results changed little; the RRs were 1.11 (95% CI, 0.76-1.61; P for trend, .48) for TNF-α–R2, 3.08 (95% CI, 2.06-4.60; P for trend <.001) for IL-6, and 2.56 (95% CI, 1.68-3.89; P for trend, <.001) for hsCRP (Table 2). The associations remained similar when we further excluded clinical cases that developed in the first year of follow-up (n = 36).
In subgroup analyses stratified by ethnicity, levels of hsCRP and IL-6 showed consistent associations with diabetes risk in all ethnic groups, while TNF-α–R2 was significantly related to diabetes only among Hispanic women (Table 3). Although only a small number of participants were Asian/Pacific Islander, a significant trend toward increased risk of diabetes associated with elevated levels of IL-6 was still apparent. We found that increasing levels of hsCRP or IL-6 were monotonically associated with increasing multivariable RRs of diabetes across the full range of plasma values of TNF-α–R2, indicating that incremental changes in hsCRP and IL-6 levels remain independently associated with diabetes risk irrespective of TNF-α–R2 levels. The increased diabetes risk tended to be additive when levels of both hsCRP and IL-6 were evaluated jointly in multivariable-adjusted models (Figure). We observed no statistically significant multiplicative interactions of TNF-α–R2, hsCRP, or IL-6 with BMI (<25 and ≥25) and waist circumference (<89 and ≥89 cm) in relation to diabetes risk, although positive associations appeared more evident among women with a BMI greater than or equal to 25 for these 3 markers (Table 4).
In this large, prospective cohort of American women aged 50 to 79 years who were followed up for 5.9 years, we found that high circulating levels of hsCRP, IL-6, and TNF-α–R2 at baseline were significantly associated with increased diabetes risk. These associations were independent of traditional risk factors, including several measures for obesity and insulin resistance. When all 3 biomarkers were mutually adjusted, hsCRP and IL-6 levels remained significantly associated with diabetes risk in all ethnic groups.
These findings support the hypothesis that chronic inflammation plays an important role in the development of type 2 diabetes in women. Although substantial variability has been observed for different inflammatory markers in relation to diabetes risk, accumulating evidence supported an independent role of inflammatory markers in affecting type 2 diabetes.19- 25,27- 33 Levels of hsCRP (ie, elevated levels of CRP well below the conventional clinical upper limit of 10 mg/L [to convert C-reactive protein to nanomoles per liter, multiply by 9.524] as measured by high-sensitivity assays) has been the main focus of previous work. Of 11 prospective studies,19- 21,23- 26,28,30,32,33 7 reported a significant positive association between hsCRP levels and diabetes risk independent of BMI,19,23- 25,28,30,33 waist-hip ratio,23,25,28,33 or measures of insulin resistance.19,23- 25,33 The RRs were in the range of 1.30 to 5.5 when the highest category of hsCRP levels was compared with the lowest category of hsCRP levels. Consistent with reports of these previous studies, hsCRP was a significant and strong risk marker for diabetes in our cohort of postmenopausal women. Human CRP is primarily synthesized by hepatocytes and regulated by inflammatory cytokines (mostly TNF-α and IL-638); however, whether hsCRP itself directly influences insulin resistance or diabetes remains speculative. Measurement of hsCRP levels is sensitive and robust compared with other inflammatory markers (with a long plasma half-life of 18 to 20 hours, a relatively low degree of intraindividual variability, and no circadian variation). Interleukin 6 is produced in a variety of tissues, including activated leukocytes, adipocytes, and endothelial cells.39,40 Our prospective data also indicate that IL-6 levels may predict risk of type 2 diabetes in these postmenopausal women.
Tumor necrosis factor α has been shown to directly inhibit insulin signaling and may thus be a critical mechanism whereby adiposity induces peripheral insulin resistance. Plasma levels of TNF-α–R2 are increasingly used as a reliable surrogate marker for TNF-α because soluble TNF-α–R2 is easily and reliably measured in frozen plasma with greater sensitivity and reliability than TNF-α alone. Prospective data directly linking TNF-α to diabetes risk are scarce. One previous study found that elevated levels of TNF-α were associated with increased diabetes risk but were not independent of BMI or waist-hip ratio.31 In a small study of Pima Indians, TNF-α was not related to diabetes risk.26 We observed a moderately increased diabetes risk associated with elevated levels of soluble TNF-α–R2, although this association was no longer significant when hsCRP and IL6 were simultaneously adjusted for. In the current study, a positive association between TNF-α–R2 and diabetes risk was more evident in Hispanic women than in other ethnic groups, although this finding could have been caused by chance, and a formal test for multiplicative interaction showed no significance. The reasons for such ethnicity-specific differences are unknown and warrant consideration in further studies.
In the past decade, considerable progress has been made in our understanding of the mechanisms underlying the relationship of systemic inflammation with the development of type 2 diabetes. In vitro studies have shown that hsCRP can induce endothelial dysfunction by down-regulating the expression of endothelial nitric oxide synthase,41,42 eliciting overproduction of endothelial adhesion molecules that promote insulin resistance.43,44 As the principal downstream mediator of the acute phase response, hsCRP may account for the integrated effects of both TNF-α and IL-6. Inhibition of insulin receptor signaling is a central mechanism through which obesity-related metabolic and inflammatory stresses induce insulin resistance.4,5 Alternatively, reverse causation may also be possible. An insulin resistance state may facilitate hepatic hsCRP production because insulin has anti-inflammatory effects and resistance to this effect would then lead to increased synthesis of CRP.45 It is also possible that mildly impaired glucose status without clinical diagnosis may elicit oxidative stress and production of free fatty acids that may raise levels of hsCRP. The ultimate resolution of the temporality of these interrelationships among inflammation, endothelial dysfunction, and insulin resistance will come from experiments that are designed to examine the temporal sequence of events at the molecular levels. Nevertheless, if systemic inflammation is causally linked to diabetes development, anti-inflammatory treatment may be of benefit. Indeed, accumulating evidence indicates that interventions aimed at improving insulin resistance, such as pharmacologic treatments with salicylates, 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors, angiotensin-converting enzyme inhibitors, and peroxisome proliferator-activated receptor γ agonists, and lifestyle interventions, such as exercise and weight loss, may reduce hsCRP levels.46 These observations offer insights into the therapeutic potential of targeting systemic inflammation for prevention and/or treatment of insulin resistance and clinical diabetes.
Several issues regarding our study design need to be recognized. First, potential biases due to the inclusion of undiagnosed diabetes in the controls must be considered. As a continuous progressive metabolic disorder, the asymptomatic pathologic process of type 2 diabetes may begin years before clinical diagnosis is possible. Because the choice of timing for clinical diagnosis is defined by a somewhat arbitrary standard, underdiagnosed diabetes is inevitable (as in any large population studies of chronic diseases). To avoid potential bias associated with the timing of outcome definition, we took careful steps to ensure comparable follow-up by (1) using standard protocols to define cases and controls and (2) matching each case-control pair for age, clinical center, and follow-up time. This stringent strategy applied in a well-defined prospective setting, coupled with multiple sensitivity analysis, makes it highly unlikely that underdiagnosis may differ according to markers of our interest. Furthermore, while our focus on clinical cases with uses of diabetic medications may lead to misclassification of diabetics in the nondiabetic groups, it nevertheless serves to minimize false-positive diagnoses. It can be shown that if the specificity of diagnosis is 100% (ie, all the cases defined are true cases), underdiagnosis would have little impact on the RR measure of associations between exposures and disease. Our results were altered little when baseline fasting glucose and insulin levels were controlled for (through exclusion or adjustment in multivariable models). Although we cannot exclude the possibility of residual confounding by incompletely measured or unmeasured covariates (eg, even after careful adjustment for BMI and waist-hip ratio), it seems unlikely that more complete statistical adjustment would completely eliminate the associations that were consistently observed across diverse subgroups.
In summary, elevated levels of hsCRP and IL-6 were significantly associated with increased diabetes risk in this cohort of multiethnic postmenopausal women. These prospective data support the notion that inflammatory markers can be used in identifying individuals with greater risk of developing diabetes and may have therapeutic implications for diabetes prevention and treatment.
Correspondence: Simin Liu, MD, ScD, Departments of Epidemiology and Medicine, University of California, Los Angeles, School of Public Health and David Geffen School of Medicine, Box 951772, 650 Charles E. Young Dr S, Los Angeles, CA 90095-1772 (firstname.lastname@example.org).
Accepted for Publication: April 11, 2007.
Author Contributions:Study concept and design: Liu, Howard, Hotamisligil, Hu, and Manson. Acquisition of data: Liu, Rifai, Bonds, Heiss, and Manson. Analysis and interpretation of data: Liu, Tinker, Song, Cook, Hu, Kuller, and Manson. Drafting of the manuscript: Liu. Critical revision of the manuscript for important intellectual content: Liu, Tinker, Song, Rifai, Bonds, Cook, Heiss, Howard, Hotamisligil, Hu, Kuller, and Manson. Statistical analysis: Liu, Song, Cook, and Kuller. Obtained funding: Liu and Manson. Administrative, technical, and material support: Liu, Tinker, Rifai, Howard, and Manson. Study supervision: Liu, Howard, Hotamisligil, Kuller, and Manson.
Financial Disclosure: Dr Manson is listed as a coinventor on a pending patent held by Brigham and Women’s Hospital that relates to inflammatory biomarkers in diabetes prediction.
Funding/Support: The Women's Health Initiative (WHI) program is funded by the National Heart, Lung, and Blood Institute, US Department of Health and Human Services (for a short list of the WHI Investigators, see below). This study was supported by National Institute of Diabetes and Digestive and Kidney Diseases R01 grant DK062290 from the National Institutes of Health.
National Heart, Lung, and Blood Institute, Bethesda, Maryland: Elizabeth Nabel, Jacques Rossouw, Shari Ludlam, Linda Pottern, Joan McGowan, Leslie Ford, and Nancy Geller.
Clinical Coordinating Centers
Fred Hutchinson Cancer Research Center, Seattle, Washington: Ross Prentice, Garnet Anderson, Andrea LaCroix, Charles L. Kooperberg, Ruth E. Patterson, Anne McTiernan; Wake Forest University School of Medicine, Winston-Salem, North Carolina: Sally Shumaker; Medical Research Labs, Highland Heights, Kentucky: Evan Stein; University of California at San Francisco: Steven Cummings.
Albert Einstein College of Medicine, Bronx, New York: Sylvia Wassertheil-Smoller; Baylor College of Medicine, Houston, Texas: Jennifer Hays; Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts: JoAnn Manson; Brown University, Providence, Rhode Island: Annlouise R. Assaf; Emory University, Atlanta, Georgia: Lawrence Phillips; Fred Hutchinson Cancer Research Center: Shirley Beresford; George Washington University Medical Center, Washington, DC: Judith Hsia; Los Angeles Biomedical Research Institute at Harbor–UCLA Medical Center, Torrance, California: Rowan Chlebowski; Kaiser Permanente Center for Health Research, Portland, Oregon: Evelyn Whitlock; Kaiser Permanente Division of Research, Oakland, California: Bette Caan; Medical College of Wisconsin, Milwaukee: Jane Morley Kotchen; MedStar Research Institute–Howard University, Washington, DC: Barbara V. Howard; Northwestern University, Chicago/Evanston, Illinois: Linda Van Horn; Rush Medical Center, Chicago, Illinois: Henry Black; Stanford Prevention Research Center, Stanford, California: Marcia L. Stefanick; State University of New York at Stony Brook: Dorothy Lane; The Ohio State University, Columbus: Rebecca Jackson; University of Alabama at Birmingham: Cora E. Lewis; University of Arizona, Tucson/Phoenix: Tamsen Bassford; University at Buffalo, Buffalo, New York: Jean Wactawski-Wende; University of California at Davis, Sacramento: John Robbins; University of California at Irvine: F. Allan Hubbell; University of California at Los Angeles: Howard Judd; University of California at San Diego, LaJolla/Chula Vista: Robert D. Langer; University of Cincinnati, Cincinnati, Ohio: Margery Gass; University of Florida, Gainesville/Jacksonville: Marian Limacher; University of Hawaii, Honolulu: David Curb; University of Iowa, Iowa City/Davenport: Robert Wallace; University of Massachusetts–Fallon Clinic, Worcester: Judith Ockene; University of Medicine and Dentistry of New Jersey, Newark: Norman Lasser; University of Miami, Miami, Florida: Mary Jo O’Sullivan; University of Minnesota, Minneapolis: Karen Margolis; University of Nevada, Reno: Robert Brunner; University of North Carolina, Chapel Hill: Gerardo Heiss; University of Pittsburgh, Pittsburgh, Pennsylvania: Lewis Kuller; University of Tennessee, Memphis: Karen C. Johnson; University of Texas Health Science Center, San Antonio: Robert Brzyski; University of Wisconsin, Madison: Gloria E. Sarto; Wake Forest University School of Medicine: Denise Bonds; Wayne State University School of Medicine–Hutzel Hospital, Detroit, Michigan: Susan Hendrix.